This project supports research to improve methods for estimating absolute and attributable risk and collaborative studies to estimate such risks for various cancers. We found that mammographic density was a promising predictor of breast cancer risk using data from the Breast Cancer Detection and Demonstration Project (BCDDP), and we gathered additional information on mammographic density from BCDDP to construct a risk model that incorporates this factor. We validated a previously developed model for projecting breast cancer risk using independent data from the Breast Cancer Prevention Trial. We incorporated a version of this model for projecting the risk of invasive breast cancer into a computer program that also offers other medical information useful in deciding whether or not to take tamoxifen to prevent breast cancer. This material has been distributed widely on diskette by NCIs Office of Cancer Communications. We published decision diagrams and algorithms based on individualized estimates of breast cancer risk to help women in their forties decide whether to begin regular screening with mammography. We analyzed the strengths and weaknesses of the kin-cohort design for estimating the risk of disease from an autosomal dominant gene. This design requires somewhat smaller sample sizes than competing designs. It can be implemented rapidly, and it yields information on genetic effects for several diseases from a single study. The kin-cohort design is subject to certain biases, however, including ascertainment bias and bias that results from ignoring non-genetic sources of intrafamilial correlation. We developed methods to analyze data from a population-based case-control study with cluster sampling to estimate the absolute and relative risk of non- melanoma skin cancer.